Dynamic Security Exposure Assessment With ML-Based Exposure Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current exposure assessment models for financial institutions are inefficient and ineffective in capturing relevant and current information about potential security exposures due to outdated methods, lack of comprehensive data capture, and fragmented data sources, leading to disjointed and inefficient processes for generating and deploying exposure assessment models.
Innovation Solution
An adaptive system that aggregates data from various public and private sources, uses machine learning to identify exposure vectors, dynamically generates tailored exposure assessment models, deploys them within the third party's network environment, and continuously monitors for changes, providing real-time notifications and compliance alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static exposure factor questionnaires are used for security exposure assessment, then the assessment process is simple and straightforward, but the assessment results become outdated and inaccurate due to the rapidly evolving technological landscape
Solution Approach 1:
The patent transforms the static questionnaire into a dynamic system that continuously updates exposure assessments by monitoring changes in third-party relationships, contractual obligations, and security controls. The system automatically re-evaluates exposure factors when changes are detected, ensuring assessment accuracy keeps pace with evolving technological and business conditions without requiring manual intervention at each stage.
Solution Approach 2:
The system implements continuous feedback loops where assessment results are monitored, and when changes in third-party relationships or security controls are detected, the system automatically triggers re-assessment. This feedback mechanism ensures that assessment accuracy is maintained over time by continuously incorporating new information about emerging risks and changing business relationships.
2Measurement precision
If comprehensive data from multiple public and private sources is aggregated, then the exposure assessment becomes more accurate and relevant, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the complex data aggregation task into distinct functional modules: a data aggregation subsystem that collects information from multiple public and private sources, a machine learning subsystem that processes and analyzes the aggregated data, and an exposure assessment subsystem that generates results. This segmentation allows each component to specialize in specific data processing functions, reducing overall system complexity while maintaining comprehensive data capture capabilities.
Solution Approach 2:
The patent introduces a machine learning subsystem as an intermediary between raw data aggregation and exposure assessment. This intermediary layer automatically processes, validates, and structures data from diverse sources before feeding it into the assessment engine, thereby managing system complexity through automated intelligence rather than manual data processing procedures.
3Productivity
If manual processes are used for generating and deploying exposure assessment models, then the process is easier to implement initially, but the productivity and speed of assessment deployment are significantly reduced
Solution Approach 1:
The patent enables the exposure assessment system to be self-updating and self-deploying. The machine learning subsystem automatically processes new data, recalibrates exposure models, and the system autonomously deploys updated assessments without requiring manual intervention for each deployment cycle. This self-service capability dramatically increases productivity while managing automation complexity through standardized automated procedures.
Solution Approach 2:
The system performs preliminary data aggregation, validation, and model training in advance so that when exposure assessments are needed, the processing is already complete or near-complete. This preliminary action allows rapid deployment of assessments without sacrificing accuracy, as the heavy processing work has been performed beforehand using automated procedures.
4Measurement precision
If static questionnaires lack specificity regarding unique relationships between financial institutions and third parties, then the questionnaire is easier to standardize, but the assessment results fail to capture contractual obligations and regulatory disclosures
Solution Approach 1:
The patent applies local quality by customizing exposure assessment parameters based on specific third-party relationships, contractual obligations, and regulatory requirements. Rather than using a uniform questionnaire for all third parties, the system dynamically adjusts assessment criteria to match the unique characteristics of each relationship, capturing specific risks associated with different contractual arrangements and regulatory contexts while maintaining overall system standardization.
Data Source
AI summary
Systems, computer program products, and methods are described herein for dynamic security exposure assessment. The present disclosure is configured to query a variety of public and private sources to capture relevant exposure information about a third party. Machine learning models analyze this information to identify key exposure vectors. Based on these vectors, an exposure assessment model is dynamically generated and deployed within the third party's network environment to evaluate their security posture. This approach enables real-time, comprehensive assessment of third-party security exposures by integrating advanced data aggregation and machine learning techniques.